Debottlenecking cogeneration systems under process variations: Multi-dimensional bottleneck tree analysis with neural network ensemble
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU143851" target="_blank" >RIV/00216305:26210/21:PU143851 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0360544220322751" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0360544220322751</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.energy.2020.119168" target="_blank" >10.1016/j.energy.2020.119168</a>
Alternative languages
Result language
angličtina
Original language name
Debottlenecking cogeneration systems under process variations: Multi-dimensional bottleneck tree analysis with neural network ensemble
Original language description
Due to lucrative economics and energy policies, cogeneration systems have blossomed in many existing industries and became their backbone technology for energy generation. With ever-increasing energy demands, the required capacity of cogeneration gradually grows yearly. This situation unveils a crawling problem in the background where many existing cogeneration systems require more energy output than their allocated design capacity. To debottleneck cogeneration systems, this work extends the bottleneck tree analysis (BOTA) towards multi-dimensional problems with novel consideration of data-driven uncertainty modelling and multi-criteria planning approaches. First, cogeneration systems were modelled using an ensemble neural network with mass and energy balance to quantify the system uncertainty while assessing energy, environment, and economic indicators in the system. These indicators are then evaluated using a multi-criteria decision making (MCDM) method to perform bottleneck tree analysis (BOTA), which identifies optimal pathways to plan for debottlenecking projects in a multi-train cogeneration plant case study. With zero initial investment and only reinvestments with profits, the method achieved 54.2 % improvement in carbon emission per unit power production, 46.3 % improvement in operating expenditure, 59.0 % improvement in heat energy production, and 58.9 % improvement in power production with a shortest average payback period of 93.9 weeks.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20303 - Thermodynamics
Result continuities
Project
<a href="/en/project/EF16_026%2F0008413" target="_blank" >EF16_026/0008413: Strategic Partnership for Environmental Technologies and Energy Production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy
ISSN
0360-5442
e-ISSN
1873-6785
Volume of the periodical
215
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
19
Pages from-to
„119168-1“-„119168-19“
UT code for WoS article
000596834000016
EID of the result in the Scopus database
2-s2.0-85095748401